Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets

Key points

• Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort.1
• We evaluate our method on four object detection benchmarks with soft-label annotations and demonstrate that high-quality annotations can be obtained with only a small fraction of manually labeled objects.1
• Furthermore, we show that detectors trained on semi-automatically annotated data achieve performance comparable to models trained on fully manually annotated data.1
• These results highlight the potential of open-vocabulary models and graph-based label propagation for efficient, accurate and uncertainty-aware pseudo-labeling.1

Sources (1)

• [1]Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:15 PM

  We address this challenge by introducing a semi-supervised framework for the uncertainty-aware annotation of object detection datasets.

  Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort.

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